Induction Of Decision Trees : Analysis

This theory which was initially used to show how systems gain knowledge by continuously interacting with users or other machines was

One of the hottest topics that modern science has been focusing on for a long time is the field of artificial intelligence, the study of intelligence in machines or, according to Minsky, “the science of making machines do things that would require intelligence if done by men”.(qtd in Copeland 1). Artificial Intelligence has a lot of applications and is used in many areas. “We often don’t notice it but AI is all around us. It is present in computer games, in the cruise control in our cars and the servers that route our email.” (BBC 1). Different goals have been set for the science of Artificial Intelligence, but according to Whitby the most mentioned idea about the goal of AI is provided by the Turing Test. This test is also called the

After the end of WWI the roaring 20’s came to life, the economy was booming, and the U.S. felt a sense of pride. Sadly though all was not as grand as it seemed the land was stained with blood from racial issues. In the book The Learning Tree by Gordon Parks a coming to age story Newt Winger tells the tale of “how it feels to be black in the white man’s world.” He faces the struggle of the racial issue as he grows up in his little town of Cherokee Flats. The young twelve year old lives in a society where blacks are slowly being integrated but are still left with the shortest straw; not being allowed to eat or work in some places and the high schools black students are not allowed on the football nor the baseball teams while a certain teachers

Artificial intelligence is the development of a computer system that is able to perform tasks of human intelligence like visual perception, speech recognition, and decision-making. Computer scientists have made a substantial advancement in the

Today at Learning Tree, it was brought to my attention that Amiah and another little girl named Isabella (Amiah told me her name and that she was also in the same class), I believe it is, have been exchanging minor verbal altercations that were witnessed and dealt with by the classroom staff. However, it became an issue today when Isabella told her parents Amiah had been bullying her. The staff member informed me of this and that on the contrary of what the child said; it was more mutual words being exchanged with the other child initiating the conversation. I just wanted to inform you on what was going on and that I have instructed Amiah and her Learning Tree teacher agreed for the afternoon program to move away from her at all times to cut

In this example, the benefit we previously calculated for 'new product, thorough development ' was $420,400. We estimate the future cost of this approach as $150,000. This gives a net benefit of $270,400.

Decision tree analysis known as an analytical tool applied to decision-making under condition of uncertainty, also clarifying where there are many possible outcomes for various alternatives and some outcomes are dependent on previous outcomes. However, decision tree will present as a diagram by showing the relationship among possible courses of action, possible events and the potential outcomes for each course of action in the decision (Drury, 2012). So decision tree analysis is useful for merchant navy company to understand in what direction their chance events are and what their values in terms of profits and losses are for each of the two tooling alternatives, also visualize the outcomes of different prospects

Rule induction is one of the major forms of data mining and is perhaps the most common form of knowledge discovery in unsupervised learning systems. It is also perhaps the form of data mining that most closely resembles the process that most people think about when they think about data mining, namely “mining” for gold through a vast database. The gold in this case would be a rule that is interesting - that tells you something about your database that you didn’t already know and probably weren’t able to explicitly articulate (aside from saying “show me things that are interesting”). The bane of rule induction systems is also its strength - that it retrieves all possible interesting patterns in the database. This is strength in the sense that it leaves no stone unturned but it can also be viewed as a weakness because the user can easily become overwhelmed with such a large number of rules that it is difficult to look through all of them. You almost need a second pass of data mining to go through the list of interesting rules that have been generated by the rule induction system in the first place in order to find the most valuable gold nugget amongst them all. This overabundance of patterns can also be problematic for the simple task of prediction because all possible patterns are culled from the database there may be conflicting predictions made by equally interesting rules. Automating the process of culling the most interesting rules and of combing the recommendations

A decision tree is a widespread technique of designing and envisaging predictive patterns and systems. It is a tree-structured design of a set of aspects to test in direction to expect the output. Decision trees are effective and accepted implements for prediction and classification. The value of decision trees is because of the reality that, in compare to neural networks, it signifies rules. Rules can quickly be articulated so that individuals can comprehend them or even directly use up in a database retrieve language as structured query language (SQL) so that keep information falling into a certain sort may be accessed. Decision tree technique is mostly used for data classification, and it differed into 2 phases; the tree pruning and the tree structure. The training data to create a test function, conferring to various classification centered on decision tree classification process in contrast with another, it is a faster, more straightforward and easy to comprehend classification systems, simply transformed into database uncertainties benefits, and particularly in problem matters of high dimension can be incredibly decent classification outcomes. The decision tree is a classification paradigm, applied to remaining data. If we apply it to special data, for which the class is unidentified, we also get a prognostication of the class. The hypothesis is that the special data originates from the analogous dissemination as the data we get through to create

Knowledge Discovery and Data Mining are rapidly evolving areas of research that are at intersection of multiple application areas and approaches. Today no field either it belongs to computer or not, knowledge discovery is required. The loss prediction, cost estimation, identification of market moves are the common application areas where knowledge discovery is essential. Knowledge discovery is not an individual process, instead it is the combination of various session data operations that are applied in a series to extract some valuable information from dataset. Data Mining is the core part of Knowledge Discovery Process. Data Mining is the intelligent

Data mining is not another hype. Instead, the need for data mining has arisen due to the wide availability of huge amounts of data and the imminent need for turning such data into useful information and knowledge. Thus, data mining can be viewed as the result of the natural evolution of information technology.

The paper proposes an efficient decision-learning algorithm for building decision trees efficiently from two distributed confidential databases while preserving the privacy of the information that is stored in each database [3, Page1]. This paper proposes an algorithm (a privacy preserving enhancement of ID3: ID3 ϭ) [3, Page8]. We have two parties; party P1 with data base D1 and part2 with data base D2. P1 view is given by D1 and ID3 ϭ (D1 U D2) and P2 view is given by D2 and ID3 ϭ (D1 U D2). The majority of the computations is performed independently on the individual databases, thus, minimizing the amount of data sent via communication and requiring reasonable bandwidth. As data mining traditionally involves huge amounts of data, the

2. Classification stage – applying the algorithm on the dataset to get FDT (Fuzzy Decision Tree) and analyse them to get results.

This is a pedagogical algorithm, which extracts the rules in the form of decision trees. This is similar to most of the decision tree algorithms and grows the tree by recursive partitioning. At every step it stores a queue of leaves that can be further expanded to sub trees and this process is repeated until a stopping condition is met. Traditional decision trees methods have a limited number of training observations. So they only have fewer number of observations to decide upon the split and leaf node class labels but on the other hand, Trepan re-labels the original observations to the classifications made by the network. And the relabeled data will be used for the tree growing process. Additionally it can also add extra data points by mimicking the behavior of the network. It uses the network as an oracle to answer the classification queries about the newly generated data points. This way it can make sure that whenever a split node or leaf node class decision is made there are at least S_(min ) number of data points. Where S_(min )is a user specified number. Whenever we generate new data points at any particular node, we have to make sure that they satisfy all the constraints from root to the current node. One of the approach to distribute the data points over a network is to employ uniform distribution, but Trepan takes into account the distribution of data i.e. at each node it estimates the marginal distribution of data. If the data at the input is continuous then it

As argued by Carrer and Povel (2003), the generative aspect of data mining consists of the building of a model by performing one of the following types of data modelling: